Convergence in models of misspecified learning
Convergence in models of misspecified learning
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错误指定学习模型的收敛
DOI：
10.3982/te3558
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发表时间：
2021
影响因子：
1.7
通讯作者：
P. Strack
中科院分区：
文献类型：
--
作者：
Paul Heidhues;B. Kőszegi;P. Strack
我们建立收敛的信念和行动在一类一维的学习设置中，代理的模型是错误的，她选择的行动内源性的，和行动影响她如何曲解信息。我们的基于随机近似的方法依赖于两个关键特征：状态和动作空间是连续的，并且代理的后验概率允许一维汇总统计。通过一个基本模型与正常-正常的更新结构和概括，其中代理的误解的信息可以依赖于她目前的信念，在一个灵活的方式，我们表明，这些功能是兼容的一些规格的代理更新。我们的框架的应用包括学习的人谁拥有一个不正确的模型，她使用的技术或对自己过于自信，学习的代表代理人可能会误解宏观经济的结果，并学习的公司，有一个不正确的参数模型的需求。
We establish convergence of beliefs and actions in a class of one-dimensional learning settings in which the agent’s model is misspecified, she chooses actions endogenously, and the actions affect how she misinterprets information. Our stochastic-approximation-based methods rely on two crucial features: that the state and action spaces are continuous, and that the agent’s posterior admits a one-dimensional summary statistic. Through a basic model with a normal– normal updating structure and a generalization in which the agent’s misinterpretation of information can depend on her current beliefs in a flexible way, we show that these features are compatible with a number of specifications of how exactly the agent updates. Applications of our framework include learning by a person who has an incorrect model of a technology she uses or is overconfident about herself, learning by a representative agent who may misunderstand macroeconomic outcomes, and learning by a firm that has an incorrect parametric model of demand.
内生错误指定学习的极限点
影响因子：
6.1
作者：
Fudenberg, Drew;Lanzani, Giacomo;Strack, Philipp
通讯作者：
Strack, Philipp
影响因子：
6.1
作者：
Frick, Mira;Iijima, Ryota;Ishii, Yuhta
通讯作者：
Ishii, Yuhta